Maximum Extreme Flow Estimations in Historical Hydrological Series under the Influence of Decadal Variations

Round 1

Reviewer 1 Report

Under the background of global climate change, the scientific estimation of extreme flows in rivers has been the focus of attention in the academic community. The authors explored the above issue based on non-parametric statistical tests. As such, it is a topic of interest to the researchers in the related areas. However, I personally think the manuscript has the following minor limitations:

(1) In Table 1, the length of the time series should be 81 years, not 80 years.

(2) Equations (6) is wrong. Please correct it.

(3) Line 139: The authors used Equation (4) for the significance test of tn. Please confirm whether it is Equation (4) or Equation (3).

(4) Line 207: Please confirm if Equations (4) and (5) should be Equations (14) and (15).

(5) Lines 220-222: The statement is confusing. Please rephrase the sentence.

(6) Lines 258-259: The authors noted that the last 9 years of the series were incorporated into the previous long wet period, without correction. Does this treatment have an impact on the study results? The authors should discuss this question.

(7) Table 1 should be Table 3.

Author Response

All the comments are addressed in the attached file. 

Author Response File: Author Response.docx

Reviewer 2 Report

This manuscript, hydrology-1811902-peer-review-v1- entitled "The use of non-parametric statistical tests for the estimations of extreme flows in rivers," is well written and has potential, but it should be more organized. This research investigates a methodology to detect the fluctuation of long wet and dry periods in the Pardo River watershed within the State of Sao Paulo, Brazil.

In my opinion, a careful revision of the English language should be carried out as there currently are some unclear sentences. The study seems to be well designed. The methodology and results are technically sound. Discussions on the scientific and practical values of the study, the limitations of proposed models, and future work are meaningful. I recommend accepting this manuscript after major revision. The main concerns are as follows:

1)     The title section should be edited and rewritten since it is too general

2)     The first paragraph should explain more about the importance of extreme flow estimation.

3)     Quantitative results should be provided in the abstract to make it more comprehensive. Results of the study Should be added in the abstract section.

4)     Improve the keywords by including only the phrases in the whole body. It is better to avoid using phrases that are repeated in the title.

5)     More recent references might support the first and second paragraphs of the introduction. Almost all references and literature are PRETTY OLD. There is no research reference for 2021 and 2022, one paper for 2020 and one for 2018. The authors should read and use the newly published papers in their research.

6)     More literature review about the other methods is needed. The manuscript could be substantially improved by relying and citing more on recent literature about contemporary real-life case studies of sustainability and/or uncertainty, such as the followings. The introduction section is weak.

7)     I recommend providing a table containing the advantages and disadvantages of the applied methodology based on the literature review and comparing the applied and similar methodologies.

Eskandari, E., Mohammadzadeh, H., Nassery, H., Vadiati, M., Zadeh, A. M., & Kisi, O. (2022). Delineation of isotopic and hydrochemical evolution of karstic aquifers with different cluster-based (HCA, KM, FCM and GKM) methods. Journal of Hydrology, 609, 127706.

Pavlides, A., Agou, V. D., & Hristopulos, D. T. (2022). Non-parametric Kernel-Based Estimation and Simulation of Precipitation Amount. Journal of Hydrology, 127988.

Nerantzaki, S., & Papalexiou, S. M. (2021). Assessing extremes in hydroclimatology: A review on probabilistic methods. Journal of Hydrology, 127302.

Rezaei, K., & Vadiati, M. (2020). A comparative study of artificial intelligence models for predicting monthly river suspended sediment load. Journal of Water and Land Development.

Fawad, M., Cassalho, F., Ren, J., Chen, L., & Yan, T. (2022). State-of-the-Art Statistical Approaches for Estimating Flood Events. Entropy, 24(7), 898.

8)     For readers to quickly catch your contribution, it would be better to highlight significant difficulties and challenges and your original achievements to overcome them more straightforwardly in the abstract and introduction.

9)     Providing a comprehensive flowchart is highly recommended by researchers, so please add a flowchart representing the methodology in the paper.

10)  The State of Sao Paulo, Brazil, is adopted as the case study. What are other feasible alternatives? What are the advantages of adopting this case study over others in this case? How will this affect the results? The authors should provide more details on this.

11)  Please provide all software used in this study. Which software did you use to apply non-parametric tests?

12)  Tab. 3 (written table 1 in the paper) and table 4 are the most important table in the manuscript, and, unfortunately, the authors did not try to discuss it in a specific way. A comprehensive discussion emphasizing would significantly improve the paper on the table.

13)  The discussion section in the present form is relatively weak and should be strengthened with more details and justifications.

14)  Comparison of the current study with previous research could be improved by more literature review.

15)  It seems that conclusions are observations only, and the manuscript needs thorough checking for explanations given for results. The authors should interpret more precisely the results argument.

 

 

Author Response

All the comments are addressed in the attached file. 

Author Response File: Author Response.docx

Round 2

Reviewer 2 Report

The authors had a great try, and the revised manuscript could be considered for publication in the present form.